使用原生语料库训练增强库尔德语文本转语音：一种高质量 WaveGlow 声码器方法
Enhancing Kurdish Text-to-Speech with Native Corpus Training: A High-Quality WaveGlow Vocoder Approach
AI总结：
针对 Central Kurdish 低资源 TTS 问题，通过在 21 小时原生语料上训练 WaveGlow 声码器以改进 Tacotron 系统，使 MOS 达到 4.91，显著超越英文预训练基线并树立新基准。
摘要：
随着文本转语音技术的进步，从文本合成口语的能力极大地促进了对数字内容的访问。然而，由于主要缺乏语言信息和专用资源，诸如 Central Kurdish (CKB) 等低资源语言的有效 TTS 开发仍面临诸多挑战。本文通过在 21 小时的 Central Kurdish 语音语料库上训练库尔德语 WaveGlow 声码器，改进了基于 Tacotron 的库尔德语 TTS 系统，而不是使用预训练的英文声码器 WaveGlow。在目标语言语料库上进行声码器训练，是为了准确且流利地适应库尔德语中的语音和韵律变化。这些增强的有效性在于，我们的模型显著优于使用英文预训练模型的基线系统。具体而言，我们的自适应 WaveGlow 模型获得了 4.91 的惊人 MOS，为库尔德语语音合成树立了新基准。一方面，本研究为 Central Kurdish 赋予了 TTS 系统的先进特性，另一方面，也为库尔德语的其他方言及其他相关语言的进一步发展打开了大门。
英文摘要：
The ability to synthesize spoken language from text has greatly facilitated access to digital content with the advances in text-to-speech technology. However, effective TTS development for low-resource languages, such as Central Kurdish (CKB), still faces many challenges due mainly to the lack of linguistic information and dedicated resources. In this paper, we improve the Kurdish TTS system based on Tacotron by training the Kurdish WaveGlow vocoder on a 21-hour central Kurdish speech corpus instead of using a pre-trained English vocoder WaveGlow. Vocoder training on the target language corpus is required to accurately and fluently adapt phonetic and prosodic changes in Kurdish language. The effectiveness of these enhancements is that our model is significantly better than the baseline system with English pretrained models. In particular, our adaptive WaveGlow model achieves an impressive MOS of 4.91, which sets a new benchmark for Kurdish speech synthesis. On one hand, this study empowers the advanced features of the TTS system for Central Kurdish, and on the other hand, it opens the doors for other dialects in Kurdish and other related languages to further develop.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
